PT Unknown AU Lluis Gomez Marçal Rusiñol Dimosthenis Karatzas TI LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting BT 14th International Conference on Document Analysis and Recognition PY 2017 DI 10.1109/ICDAR.2017.88 AB n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset. ER